Displaying 20 results from an estimated 900 matches similar to: "pseudo-R by hand"
2009 May 16
1
maxLik pakage
Hi all;
I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error in calling gradient function;
The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector ‘h’ is
2009 Jan 05
1
transform R to C
Dear R users,
i would like to transform the following function from R-code to C-code and call it from R in order to speed up the computation because in my other functions this function is called many times.
`dgcpois` <- function(z, lambda1, lambda2)
{
`f1` <- function(alpha, lambda1, lambda2)
return(exp(log(lambda1) * (alpha - 1) - lambda2 * lgamma(alpha)))
`f2` <-
2006 Jul 07
1
convert ms() to optim()
How to convert the following ms() in Splus to Optim in R? The "Calc" function is also attached.
ms(~ Calc(a.init, B, v, off, d, P.a, lambda.a, P.y, lambda.y,
10^(-8), FALSE, 20, TRUE)$Bic,
start = list(lambda.a = 0.5, lambda.y = 240),
control = list(maxiter = 10, tol = 0.1))
Calc <- function(A.INIT., X., V., OFF., D.,
P1., LAMBDA1., P2., LAMBDA2.,
TOL., MONITOR.,
2008 Jul 25
1
transcript a matlab code in R
Dear R-users,
I am trying to translate a matlab code for calculating the Local Whittle
estimator in time series with long memory originally written by Shimotsu and
available free in his webpage (
http://www.econ.queensu.ca/pub/faculty/shimotsu/ )
The Matlab code is
=======================================================================================
function[r] = whittle(d,x,m)
% WHITTLE.M
2008 Sep 12
1
Error in "[<-"(`*tmp*`, i, value = numeric(0)) :
I use "while" loop but it produces an errro. I have no idea about this.
Error in "[<-"(`*tmp*`, i, value = numeric(0)) :
nothing to replace with
The problem description is
The likelihood includes two parameters to be estimated: lambda
(=beta0+beta1*x) and alpha. The algorithm for the estimation is as
following:
1) with alpha=0, estimate lambda (estimate beta0
2008 Sep 19
2
Error: function cannot be evaluated at initial parameters
I have an error for a simple optimization problem. Is there anyone knowing
about this error?
lambda1=-9
lambda2=-6
L<-function(a){
s2i2f<-(exp(-lambda1*(250^a)-lambda2*(275^a-250^a))
-exp(-lambda1*(250^a)-lambda2*(300^a-250^a)))
logl<-log(s2i2f)
return(-logl)}
optim(1,L)
Error in optim(1, L) : function cannot be evaluated at initial parameters
Thank you in advance
--
View this
2009 Oct 14
1
different L2 regularization behavior between lrm, glmnet, and penalized?
The following R code using different packages gives the same results for a
simple logistic regression without regularization, but different results
with regularization. This may just be a matter of different scaling of the
regularization parameters, but if anyone familiar with these packages has
insight into why the results differ, I'd appreciate hearing about it. I'm
new to
2005 Dec 09
1
O-ring statistic
Rainer M Krug writes:
> Thorsten Wiegand used in his paper Wiegand T., and K. A. Moloney 2004.
> Rings, circles and null-models for point pattern analysis in ecology.
> Oikos 104: 209-229 a statistic he called O-Ring statistic which is
> similar to Ripley's K, only that it uses rings instead of circles.
>
> http://www.oesa.ufz.de/towi/towi_programita.html#ring
2001 Sep 14
1
Supply linear constrain to optimizer
Dear R and S users,
I've been working on fitting finite mixture of negative exponential
distributions using maximum likelihood based on the example given in MASS.
So far I had much success in fitting two components. The problem started
when I tried to extend the procedure to fit three components.
More specifically,
likelihood = sum( ln(c1*exp(-x/lambda1)/lambda1 +
c2*exp(-x/lambda2)/lambda2
2012 Oct 03
1
Errors when saving output from WinBUGS to R
Dear all
I used R2WinBUGS package's bugs() function to generate MCMC results. Then I
tried to save the simulation draws in R, using read.bugs() function. Here is
a simple test:
######################
library(coda)
library(R2WinBUGS)
#fake some data to test
beta0=1
beta1=1.5
beta2=-1
beta3=2
N=200
x1=rnorm(N, mean=0,sd=1)
x2=rnorm(N, mean=0,sd=1)
x3=rnorm(N, mean=0,sd=1)
lambda2= exp(beta0+
2008 Dec 16
0
[LLVMdev] Another compiler shootout
On Tuesday 16 December 2008 01:03:36 Evan Cheng wrote:
> FYI. http://leonardo-m.livejournal.com/73732.html
>
> If anyone is motivated, please file bugs for the losing cases. Also,
> it might make sense to incorporate the tests into our nightly tester
> test suite.
FWIW, I just ported my ray tracer benchmark to C and found that llvm-gcc gives
much worse performance than gcc on x86
2012 Apr 12
2
How to calculate the "McFadden R-square" for LOGIT model?
Dear all, can somebody please help me how to calculate "McFadden
R-square" for a LOGIT model? Corresponding definition can be found
here:
http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Falg_plum_statistics_rsq_mcfadden.htm
Here is my data:
Data <- structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 1,
2017 Jun 02
1
modEvA D-squared for gamma glm
Hi All,
I am running a generalized linear model with gamma distribution in R (glm,
family=gamma ) for my data (gene expression as response variable and few
predictors). I want to calculate r-squared for this model.
I have been reading online about it and found there are multiple formulas
for calculating R2 (psuedo) for glm (in R) with gaussian (r2 from linear
model), logistic regression
2006 Jul 14
1
Optim()
Dear all,
I have two functions (f1, f2) and 4 unknown parameters (p1, p2, p3, p4). Both
f1 and f2 are functions of p1, p2, and p3, denoted by f1(p1, p2, p3) and
f2(p1,p2,p3) respectively.
The goal is to maximize f1(p1, p2, p3) subject to two constraints:
(1) c = k1*p4/(k1*p4+(1-k1)*f1(p1,p2,p3)), where c and k1 are some known
constants
(2) p4 = f2(p1, p2, p3)
In addition, each parameter
2009 Aug 25
1
Elastic net in R (enet package)
Dear R users,
I am using "enet" package in R for applying "elastic
net" method. In elastic net, two penalities are applied one is lambda1 for
LASSO and lambda2 for ridge ( zou, 2005) penalty. But while running the
analysis, I realised tht, I optimised only one lambda. ( even when I
looked at the example in R, they used only one penality) So, I am
2008 Sep 11
0
Loop for the convergence of shape parameter
Hello,
The likelihood includes two parameters to be estimated: lambda
(=beta0+beta1*x) and alpha. The algorithm for the estimation is as
following:
1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM)
2) with lambda, estimate alpha via ML estimation
3) with updataed alpha, replicate 1) and 2) until alpha is converged to a
value
I coded 1) and 2) (it works), but faced some
2012 Oct 21
0
R^2 in Poisson via pr2() function: skeptical about r^2 results
Hello.
I am running 9 poisson regressions with 5 predictors each, using glm with
family=gaussian.
Gaussian distribution fits better than linear regression on fit indices,
and also for theoretical reasons (e.g. the dependent variables are counts,
and the distribution is highly positively skewed).
I want to determine pseudo R^2 now. However, using the pR2() of the pscl
package offers drastically
2008 Feb 12
1
Finding LD50 from an interaction Generalised Linear model
Hi,
I have recently been attempting to find the LD50 from two predicted fits
(For male and females) in a Generalised linear model which models the effect
of both sex + logdose (and sex*logdose interaction) on proportion survival
(formula = y ~ ldose * sex, family = "binomial", data = dat (y is the
survival data)). I can obtain the LD50 for females using the dose.p()
command in the MASS
2009 Jul 15
0
Nagelkerkes R2N
I am interested Andrea is whether you ever established why your R2 was 1.
I have had a similar situation previously.
My main issue though, which I'd be v grateful for advice on, is why I am obtaining such negative values -0.3 for Somers Dxy using validate.cph from the Design package given my value of Nagelkerke R2 is not so low 13.2%.
I have this output when fitting 6 variables all with
2011 Apr 08
1
multinom() residual deviance
Running a binary logit model on the data
df <- data.frame(y=sample(letters[1:3], 100, repl=T),
x=rnorm(100))
reveals some residual deviance:
summary(glm(y ~ ., data=df, family=binomial("logit")))
However, running a multinomial model on that data (multinom, nnet)
reveals a residual deviance:
summary(multinom(y ~ ., data=df))
On page 203, the MASS book says that "here the